AI interface design beyond chat: what we build instead of a chat window

Almost every AI product we're asked to review opens the same way. An empty text field, a blinking cursor, and an invitation to ask anything.

There's a good reason it happens. Chat is the quickest way to get a model in front of real users. The model returns text, the interface shows text, and the team learns from what people type.

The cost shows up later. Usage trails off after the first week. The people who stay type the same handful of requests over and over. And the product starts to look like every other AI app in the category.

It's now the most common brief we hear from AI founders. Over the past year, five have come to us asking for some version of the same thing: an AI product that doesn't revolve around a chat window. Here's how we approach that work.

Why do AI products default to chat?

Chat is the cheapest interface to build. It isn't the easiest one to use.

An open prompt asks people to do the hard part themselves. They have to know what to ask, word it well and decide whether the answer can be trusted. Decades of interface design went into removing that effort with menus, defaults, suggestions and clear next steps. A blank text box quietly hands it all back.

It also hides the product. A new user can't see what the AI is good at from an empty field, so they test it with something small, get an average answer, and move on.

What we design instead

We start with the user's job. The first question we ask a founder is what their customer is in the middle of doing at the moment the AI helps. That answer usually decides the pattern.

AI in the existing workflow. When someone is drafting, editing a record or reviewing a trade, the AI appears right there as a suggestion they can accept, change or dismiss. There's no prompt to write and no new screen to visit.

Review queues. When the AI works in the background, the product becomes a list of finished work waiting for approval: drafts, updated records, scored leads. The user goes from doing the task to checking it, so the design has to make checking quick and trustworthy.

Canvases. Some work is a structure, and a structure is easier to see than to describe. For Retell, the YC-backed voice agent platform, we rebuilt the design system and core UX flows. Teams there build voice agents on a canvas that lays out every step of a call at once.

Structured output. When the answer has a shape, we show the shape. Comparisons become tables, plans become timelines and options become cards with their trade-offs visible. Long paragraphs leave the reader to do that work.

Ambient status. Agents that run for minutes or hours need a place on screen that shows what they're doing, what's done and what they need. It works like an upload progress bar, where the upload is a piece of work.

When chat is the right call

Chat still has a place. It suits open-ended questions, exploration and power users who know exactly what they want. It's also a useful fallback for requests the interface didn't anticipate.

The problem is using it as the front door. In most of our projects a chat or command input stays in the product, and the main experience is designed around the three or four jobs that make up most of the usage.

How we make an AI product feel AI-native

We read real usage before we design. If the product is live, its chat logs are the best brief available. The requests people repeat every day are the buttons, defaults and suggestions the interface is missing.

We design for mistakes. Models get things wrong, so the interface has to make errors cheap to spot and fix. That means showing sources, making edits simple and keeping it clear what the AI did and what the person did.

We prototype in code. A static mock-up can't show how an AI product feels, because the feel lives in streaming, waiting, loading states and long or unexpected answers. We build the core flows as working prototypes with real model output early, so the team reacts to the real behaviour.

We treat response time as a design decision. An answer that arrives in a second can sit inline. One that takes a minute becomes a background job the user can leave and come back to. Settling that early saves a lot of rework.

Check your own product

Find the chat box in your product and ask:

• What are the five requests people make most often?

• Could any of them be a button, a default or a suggestion on the screen where people already work?

• Can a new user tell what the product does before typing anything?

• When the AI is wrong, how many steps does it take to fix?

Most AI products we look at have at least one uncomfortable answer here. That's normal, and it's usually the clearest place to start.

Work with us

Halaska is the product design partner for funded AI and crypto teams, directed by Chris Halaska. If chat is carrying more of your product than it should, get in touch.

FAQ

Does an AI product need a chat interface? No. Chat works for open-ended tasks and expert users, but most AI products are easier to use when the AI sits inside the screens where the work already happens, with chat kept as a fallback.

What can replace a chatbot UI? The main alternatives are inline suggestions, review queues for background work, canvases for structured work, structured output such as tables and cards, and status views for long-running agents.

What does AI-native product design mean? It means designing the whole product around what the AI does well and where it fails, rather than adding a chat box to an existing app. That covers the interface, how AI work is shown and checked, and how fast each answer needs to arrive.

How should you prototype an AI interface? Build the core flows as working prototypes in code with real model output. Static designs miss streaming, waiting and wrong answers, which decide how an AI product feels.

Who designs AI products beyond chat? Halaska is a product design studio for funded teams, directed by Chris Halaska from Dubai. We design AI and crypto products as an ongoing partner, with senior designers and a founder who has led product design at Google and Immutable.